High Yields, High Efficiencies, and High Environmental ...€¦ · High Yields, High Efficiencies,...

High Yields, High Efficiencies, and High

Environmental Standards: H3 Pipe Dream?

Kenneth G. Cassman

Robert B. Daugherty Professor of Agronomy,

University of Nebraska—Lincoln, and

Chair, Independent Science and Partnership Council,

Consultative Group for International Agricultural Research

20 Feb 2012 High Yld--H Eff--H EnvirStd 1

Brave New World Since 2005

• Rapid, sustained economic growth in the most populous developing countries

• Rapid rise in petroleum prices

• Convergence of energy and agriculture

• Falling supply relative to demand for staple food prices



Energy or Cereal Consumption versus Income by Country

Naylor et al., 2007. Environment 40: 30-43. Energy and income data from World Bank development indicators; cereal consumption data from FAOSTAT.

2003-2004

each data point

represents one country energy

food

Biofuels compared to what in a world with changing climate?

Photo: Gerald Herbert/AP

Deep water petroleum? Oil sands? “Frac” natural gas? Coal? Nuclear Power?

Deepwater Horizon drilling rig

explosion and oil leak:

Gulf of Mexico, April 2010

Urban-industrial expansion onto prime farmland at the periphery

of Kunming (+6 million), the capital of Yunnan Province, China,

Photo: K.G. Cassman

Clearing virgin rain forest in Brazil: powerful +feedback to GHG emissions

Photo: K.G. Cassman


• Rapid, sustained economic growth in most populous developing countries

• Rapid rise in petroleum princes


• Falling supply relative to demand for staple food prices

• Increased poverty and malnutrition

8 20 Feb 2012 High Yld--H Eff--H EnvirStd 8

Food insecurity: unsustainable crop production on marginal

land by poor farm families without other options

Photo: K.G. Cassman

15 April 2011 Food Security and Environment 10

Photo: K.G. Cassman





• Smaller supply, relative to demand, of staple food crops; steep rise in the price of these foods

• Increasing poverty and malnutrition

• Limited supplies of good quality arable land and accessible fresh water

• Stagnating yields in some of the most productive cropping systems

• Increasing concerns about environment and climate change


100

150

200

250

300

1965 1975 1985 1995 2005

YEAR

IRR

IGA

TE

D A

RE

A (

Mh

a)

10.0

12.5

15.0

17.5

20.0

Irrigated Area

% of total

cultivated area

Global Irrigated Area and as a % of Total Cultivated

Land Area, 1966-2004

Irrigated systems occupied 18% of

cultivated land area but produced

40% of human food supply


Decreasing water supply in all major irrigated areas

In an increasingly urban world, irrigated agriculture is more

important than ever to provide “ballast” to global food supply


Also a concern are yield plateaus for several major crops.

What are the causes? Korea and China for rice, wheat in

northwest Europe and India, maize in China,

and……..perhaps also for irrigated maize in the USA??

Cassman et al, 2003, ARER 28: 315-358

Maize

China

Brazil

USA - rainfed

USA - irrigated

0

2

4

6

8

10

12

1960 1970 1980 1990 2000 2010Year

Wheat

China

India

Northwest Europe

0

1

2

3

4

5

6

7

8

1960 1970 1980 1990 2000 2010Year

Rice

India

R. Korea

China

Indonesia

0

1

2

3

4

5

6

7

1960 1970 1980 1990 2000 2010Year

Yie

ld (

Mg

ha

-1)

Yie

ld (

t h

a-1

)

Cassman, 1999. PNAS, 96: 5952-5959

?

Cassman et al., 2003, ARER 28: 315-358

Cassman et al., 2010, Handbook of Climate Change

Grassini et al., 2011. FCR 120:142-152






• Smaller supply, relative to demand, of staple food crops; steep rise in the price of these foods

• Increasing poverty and malnutrition

• Limited supplies of good quality arable land and accessible fresh water

• Stagnating yields in some of the most productive cropping systems

• Increasing concerns about environment and climate change

• These are likely to be LONG-TERM MEGATRENDS


MAIZE (CORN) (■)

slope = 64 kg ha-1

y-1

(1 bu ac-1

y-1

) r2

= 0.97

RICE (●)

slope = 52 kg ha-1

y-1

r2

= 0.99

WHEAT (▲)

y = 40 kg ha-1

y-1

r2

= 0.98

0

1

2

3

4

5

1960 1970 1980 1990 2000 2010

Year

Gra

in y

ield

(t

ha-1

)

Global Cereal Yield Trends, 1966-2009

THESE RATES OF INCREASE ARE NOT FAST ENOUGH TO MEET

EXPECTED DEMAND ON EXISTING FARM LAND! source: FAOSTAT

+2.8%

+1.3%

16


Year

1960 1970 1980 1990 2000 2010

Co

rn g

rain

yie

ld (

bu

ac

-1)

60

80

100

120

140

160

180

linear rate of gain = 1.86 bu ac-1

yr-1

double- to single-crosshybrids

Expansion of irrigated area

Conservation tilliage and soil testing

Increased N fertilizer rates

Improved balance in N,P,K fertilization

Precision planters

Transgenic (Bt)insect resistance

Electronicauto-steer

Multi-location hybrid testing in1000s of on-farm strip trials

Integrated pest management

USA Corn Yield Trends, 1966-2009

(and supporting science and technologies)

Modified from: Cassman et al. 2006. Convergence of energy and Agriculture. Council on

Agriculture, Sci. Tech. Commentary QTA 2006-3. Ames, Iowa

• A ~60% increase in cereal* yields needed

by 2050 (38 yr) = 1.54% yr-1 of current

average yield

• Business as usual will not meet 2050 global

demand for food, feed, fuel in without large

expansion of crop area

• How much help from less meat and less

post-harvest losses and food waste?

*Cereals for food, feed, fuel, bio-industrials

Assuming a goal of no net expansion of

current crop production area……


The Challenge is Clear

• Increase food supply +70% (cereals + 60%) on existing crop and pasture land

• Substantially decrease environmental footprint of agriculture

–Protect water quality and conserve water for non-agriculture uses

–Maintain or improve soil quality

–Reduce greenhouse gas emissions

–Protect wildlife and biodiversity

• Called “ecological intensification”


• Expansion of soybean production in Brazil

(2005)

• Greenhouse gas emissions from corn-

ethanol life cycle (2006-2009)

• Recent EPA report on Integrated Nitrogen

Management

• Nebraska irrigated corn production

Four Stories About H3 in Agriculture



Kenneth G. Cassman, Dept. of Agronomy, Univ. of Nebraska In: Soybean Review, Fall 2005, NE Soybean Board

• Brazilian soybean production costs are much lower than in the USA, but only because of lower fixed costs

– Does not include cost of transport to markets, which are much higher in Brazil

– Variable costs are much lower in USA due to better soils and much lower fertilizer inputs, disease and insect pressure

• Head-to-head environmental performance much better in USA

– Global concerns about clearing of rain forest in Mato Grosso and elsewhere in Brazil for expansion of crop production

– Large greenhouse gas emissions per ton of soybean production in Brazil due to large inputs and land clearing

Brazilian vs USA Soybean Production


Biofuels Case Study: from good guy to

villain in 2-years: 2005 to 2007

Benefits

Decreased reliance on imported petroleum

Net reduction in greenhouse gas (GHG) emissions

Rural jobs and economic development

Reduces cost of gasoline for consumers ($25-80B/yr)

Negative impacts and concerns

Relies on subsidies

Net increase in GHG emissions and net energy loss (energy inputs > outputs)

Uses too much water, causes land use change

Major cause of rising food prices


24

Stillage

CO2

Fertilizer offset in crop production Horticultural

uses/organic ag?

N2O CH4

CH4

manure, urine

CH4

Meat

Ethanol

Distillers grain

Grain

Grain NO3 leaching

N2O

CO2

Corn Production

--Grain and stover yields in relation

to climate and management

--All inputs and outputs have

energy and GHG equivalents

--Net impact on soil carbon balance

and nitrate/phosphate losses

(water quality concerns)

Ethanol Plant

--Energy input and outputs per

bushel of corn, total energy yield

--Energy sources (natural gas,

coal, burning biomass, biogas)

--Greenhouse emissions

--Distillers grain processing

Cattle Feedlot

--Feed, energy and other inputs

--Animal weight gain and feed efficiency

--manure output and nutrient content

--Methane, nitrous oxide, (and CO2?)

emissions

--Meat production

NO3 leaching

Methane Biodigestor

--Manure and nutrient inputs

--Methane output

--Biofertilizer output, fertilizer

replacement value, land requirement

(higher value use in horticulture?)

Biofertilizer

Life Cycle Assessment:

Integrated Biofuel Biorefinery with Corn Grain as Feedstock

CO2

20 Feb 2012 High Yld--H Eff--H EnvirStd

Purpose of LCFS

• 2007 Energy Independence and Security

Act (EISA)

– Help guide R&D prioritization & investment

• CA Low Carbon Fuel Standard

– Achieve a 10% reduction in motor fuel GHG

intensity by 2020

• Foster and reward the build-out of a

“green” biofuel industry

– GHG emissions trading, certification


26

2007 EISA definition: Life Cycle GHG Emissions


27

Biofuel Energy Systems Simulator (BESS)

[available at: www.bess.unl.edu]

• Most up to date estimates for direct-effect GHG emissions for corn ethanol based on best current science and input from all key disciplines (engineers, agronomists, soil scientists, animal nutritionists, industry professionals)

• User-friendly, completely transparent, and well documented

• Default scenarios based on regional-scale data, but can also be used for certification of an individual ethanol plant, its associated corn supply and co-product use

• Can be used for estimating carbon-offset credits for emissions trading with an individual ethanol plant as the aggregator

• If GREET can be consistent with BESS for corn-ethanol GHG emissions estimates, then BESS can be used for compliance and certification


Conclusions from BESS analysis

• Corn-ethanol systems are not accurately evaluated as an

aggregate, due to differences in biorefinery designs, energy

sources, and crop production practices

• Based on state records and new surveys, natural gas powered dry

mills (88% of the industry) can reduce GHG emissions by 48-59%

compared to gasoline on average, which is a 2-3 fold greater

reduction than reported in previous studies

• Crop production represents 42-51% of life-cycle GHG emissions

for typical USA corn-ethanol systems; needs accurate assessment

• Co-product credits offset 26-38% of life-cycle GHGs

• Accurate GHG analysis is essential for enabling ethanol producers

to meet the 20% GHG emissions reduction relative to gasoline for

the EISA of 2007, and will be critical for state-level LCFS

Published in 2009: Liska AJ, Yang HS, Bremer VR, Klopfenstein TJ, Walters DT,

Erickson GE, Cassman KG. Improvements in Life Cycle Energy Efficiency and

Greenhouse Gas Emissions of Corn-Ethanol. J. Industrial Ecol. 13:58-74

31

Most sensitive input parameters on GHG emissions [as well as on net energy yield]

1. Crop yield: Mg per hectare or bushels per acre:

tremendous scope for improvement

2. Conversion yield: liters ethanol per lb grain: little scope for improvement

3. Conversion thermal energy inputs: MJ per L: little scope for improvement

4. Reduced drying of distillers grains (i.e. use

more for local livestock or dairy production)

5. Increased N fertilizer use efficiency in corn production


Objectives


Released:

October, 2011


FINDINGS:


Recommendations

• Because reactive nitrogen (Nr) flows through multiple ecosystems (land,

surface and groundwater, estuaries) and in many different forms (NH4, NO3,

N2O), new institutional structures are needed for effective control and

management

• Requires integrated management that recognizes complex tradeoffs, are

cost-effective, and identifies key intervention points

• EPA Intra-agency task force recommended to: (i) better quantify Nr impacts

on ecosystems, human health, climate change, (ii) monitoring needs to

support informed policies, (iii) identify most efficient and cost-effective ways

to reduce Nr volumes and negative Nr impacts on environment, HH, CC.

• Inter-agency task force needed (EPA, USDA, DOE, NSF, DOT, etc) to

coordinate “all of government” efforts

Take Home on EPA Nr Study

• Nr in form of commercial fertilizer is critical to ensure global food security

• There is too much reactive N in the global environment, and it causes degradation of water quality, biodiversity, and has health concerns

• Majority of Nr in the environment comes from agriculture

• Recommends increased monitoring to identify best mitigation interventions


On-farm analysis: maize

fields in the Tri-Basin NRD

--- Data from 3 years (2005, 2006, and 2007)

--- 777 field-year data identified with 100%

irrigated maize

20 Feb 2012 37

Land allocation and average yields in Tri-Basin NRD (2000-2008, USDA-NASS)

Total cropland area: 690,110 acres

Crop Yield (bu ac-1)*

Corn 180.0

Soybean 56.1

Wheat 46.5

Sorghum 73.7

* Includes both rainfed and irrigated crops

Sorghum

1%

Soybean

29%

Crops for

silage/hay

9%Wheat

5%

Grain Corn

55%


38

Tri-Basin NRD: irrigation system, rotation, and tillage

Irrigation system (n = 777)

GRAVITY

(33%)

PIVOT

(49%)

MIXED

(pivot and gravity in

the corners of the field)

(18%)

Crop rotation (n = 777)

OTHERS (1%)

(wheat, sorghum,

millet)

CONTINUOUS

CORN (38%)

SOYBEAN-

CORN (61%)

STRIP-TILL (10%)

NO-TILL

(37%)

DISK

(22%)

RIDGE-

TILL

(31%)

Tillage system (n = 123)

(2%)

DIESEL 26%

ETHANOL

NATURAL

GAS (49%)

PROPANE (2%)

ELECTRICITY (21%)

Energy source for irrigation (n = 777)

Grain yield: effects of hybrid maturity x sowing date

Planting date and hybrid

maturity effects were

significant at p=0.05 and

0.07, respectively.

185

190

195

200

205

210

215

220

3rd-wk April

4th-wk April

1st-wk May

2nd-wk May

Gra

in y

ield

(b

u a

c-1

)

Short-season hybrids

Full-season hybrids

Modified from Grassini et al. (2010): Field Crops Res.

* Based on management data collected from 123 fields in the Tri-Basin NRD during 2005-2007 seasons.

** Data were aggregated into two hybrid maturity categories [short- (106-112 days) and full-season (113-118

days)] and four sowing date 7-day intervals. Vertical bars indicate ±SE of the mean

High Yld--H Eff--H EnvirStd

Effect of previous crop and tillage

* number of observations is indicated inside bars; ** vertical bars indicate ±SE of the mean; *** in the

second figure, data were pooled across years. Selected t-test comparisons are shown.

Large yield advantage in

cont. corn but little

benefit when corn

follows soybean (Tillage

x previous crop

interaction significant)

Yield advantage of

soybean/corn rotation

over continuous corn

was consistent across

years, but..,,,,,,,,,,,,,

180

190

200

210

220

230

240

2005 2006 2007

Year

Δ yield = 11 bu ac-1

p <0.0001


p<0.05


p<0.0001

Corn-soybean (S-C)

Continuous corn (C-C)

Crop sequence:A

ctu

al

maiz

e y

ield

(b

uac

-1)

180

190

200

210

220

230

240

Continuous corn Soybean-corn

10 14 10 26 21 12

Ridge-till (RT)

Disk (D)

No-till (NT)

Tillage system:D vs. RT under C-C: ∆=10 bu ac-1; p<0.05

D vs. NT under C-C: ∆=16 bu ac-1; p<0.005

C-C vs. S-C in NT: ∆=16 bu ac-1; p<0.001

61 146 78 129 139 189

180

190

200

210

220

230

240

2005 2006 2007

Year


p <0.0001


p<0.05


p<0.0001

Corn-soybean (S-C)

Continuous corn (C-C)

Crop sequence:A

ctu

al

maiz

e y

ield

(b

uac

-1)

180

190

200

210

220

230

240


10 14 10 26 21 12

Ridge-till (RT)

Disk (D)

No-till (NT)

Tillage system:D vs. RT under C-C: ∆=10 bu ac-1; p<0.05

D vs. NT under C-C: ∆=16 bu ac-1; p<0.005

C-C vs. S-C in NT: ∆=16 bu ac-1; p<0.001

61 146 78 129 139 189

Grassini et al. (2011): Field Crops Res.

Simulated corn yield / water supply relationship

*Available soil water (0-5 ft) at planting + planting-to-maturity rainfall + applied irrigation

Yields were simulated over 20-y

for 18 locations in the Western

Corn-Belt using Hybrid-Maize

model (Yang et al., 2004). Crops

assumed to grow under optimal

conditions (no nutrient

deficiencies and no incidence of

pests, diseases, and weeds).

Model inputs based on actual

sowing date, plant population,

weather data, and soil properties

for each of the 18 locations. Gra

in y

ield

(b

uac

-1)

0

50

100

150

200

250

0 5 10 15 20 25 30 35 40 45 50

Seasonal water supply* (in)

Water productivity

(WP) boundary

(11 bu ac-in-1)

Mean WP function

(8 bu ac-in-1)

Gra

in y

ield

(b

uac

-1)

0

50

100

150

200

250

0 5 10 15 20 25 30 35 40 45 50

Seasonal water supply* (in)

Water productivity

(WP) boundary

(11 bu ac-in-1)

Mean WP function

(8 bu ac-in-1)



Reported grain yield** / water supply data collected

in the Western Corn-Belt by UNL researchers

** Crop grown under near-optimal management practices

Gra

in y

ield

(b

uac

-1)

WP boundary

slope = 11 bu ac-in-1

Mean WP function


North Platte, NE, 1996-2006 (Payero

et al., 2006, 2008).

0

50

100

150

200

250

0 10 20 30 40 50

Seasonal water supply (in)

n = 123Mead, NE, 2001-2006. High intensive

management (Suyker and Verma, 2009)

Progressive farmer fields in Eastern

Nebraska, 2007-2008 (Burgert, 2009)

North Platte, NE, 1983-1991 (Hergert

et al., 1993).

North Platte and Clay Center, NE,

2005-2006 (Irmak and Yang,

unpublished data).

Farmer field winner of National Corn

Grower yield contest. Manchester, IA,

2002 (Yang et al., 2004).

Rainfed

Sprinkler

irrigation

Subsurface

drip irrigation

Gra

in y

ield

(b

uac

-1)

WP boundary


Mean WP function


North Platte, NE, 1996-2006 (Payero

et al., 2006, 2008).

0

50

100

150

200

250

0 10 20 30 40 50


n = 123Mead, NE, 2001-2006. High intensive

management (Suyker and Verma, 2009)

Progressive farmer fields in Eastern

Nebraska, 2007-2008 (Burgert, 2009)

North Platte, NE, 1983-1991 (Hergert

et al., 1993).

North Platte and Clay Center, NE,

2005-2006 (Irmak and Yang,

unpublished data).

Farmer field winner of National Corn

Grower yield contest. Manchester, IA,

2002 (Yang et al., 2004).

Rainfed

Sprinkler

irrigation

Subsurface

drip irrigation



Analytical framework to benchmark and analyze water

productivity in farmers’ fields

0

45

90

135

180

225

270

0 10 20 30 40 50


Gra

in y

ield

(b

ua

c-1

)

WP boundary-function

slope = 11 bu acre-in-1

Mean-WP function

slope = 8 bu-in-1

x-intercept ≈ 4 in

(a)

(b)

(c) Improved crop mgt

Improved irrigation mgt

0

45

90

135

180

225

270

0 10 20 30 40 50


Gra

in y

ield

(b

ua

c-1

)

WP boundary-function

slope = 11 bu acre-in-1

Mean-WP function

slope = 8 bu-in-1

x-intercept ≈ 4 in

(a)

(b)

(c) Improved crop mgt

Improved irrigation mgt


On-farm water productivity

at the Tri-Basin NRD

120

160

200

240

280

15 25 35 45 55


Actu

al

gra

in y

ield

(b

u a

c-1

)

SURFACE

** Yield data based on farmer-reported values to the Tri-

Basin NRD, 2005-2007. Each data point corresponds to a

site-year crop.

120

160

200

240

280

15 25 35 45 55

PIVOT n = 516

n = 261

Ac

tua

l g

rain

yie

ld (

bu

ac

-1)

0

40

80

120

160

200

240

280

0 5 10 15 20 25 30 35 40 45 50 55


WP boundary

11 bu ac-in-1

Mean-WP function


n = 777 Yield potential

246 bu ac-1


Average farmer’s

WP = 5.8 bu ac-in-1

Water requirement

for maximum yield

~ 35.5 in

WP = 6.0

bu ac-in-1

WP = 5.3

bu ac-in-1

45

Effect of irrigation system and tillage

* number of observations is indicated inside bars; ** vertical bars indicate ±SE of the mean; *** in the

second figure, data were pooled across years. Selected t-test comparisons are shown.

Applied irrigation was

higher (-4.5”) in

gravity than in pivot

systems in all years

No yield difference!

Applied irrigation

under ridge- and no-

till was lower than

under disk (-3.0”)

0

5

10

15

20

25

2005 2006 2007

Year

Ap

pli

ed

irr

iga

tio

n (

in)

0

5

10

15

20

25

Surface Pivot

Pivot

Surface

Pivot

Irrigation system:

Ridge-till (RT)

Disk (D)

No-till (NT)

Tillage system:

Δ = 6.2 in

p < 0.0001

Δ = 3.8 in

p < 0.0001 Δ = 3.5 in

p = 0.0001

D vs. RT and NT in S: ∆=3.0 in; p = 0.08

D vs. RT and NT in P: ∆=3.1 in; p < 0.005

13 17 8 9 9 30

70 110 66 105 155 111

0

5

10

15

20

25

2005 2006 2007

Year

Ap

pli

ed

irr

iga

tio

n (

in)

0

5

10

15

20

25

Surface Pivot

Pivot

Surface

Pivot

Irrigation system:

Ridge-till (RT)

Disk (D)

No-till (NT)

Tillage system:

Δ = 6.2 in

p < 0.0001

Δ = 3.8 in

p < 0.0001 Δ = 3.5 in

p = 0.0001

D vs. RT and NT in S: ∆=3.0 in; p = 0.08

D vs. RT and NT in P: ∆=3.1 in; p < 0.005

13 17 8 9 9 30

70 110 66 105 155 111



● Simulated yield under limited-

irrigation management (75% of

fully-irrigation except during the

interval around silking when the

crop was fully-irrigated)

■ Simulated yield under fully-

irrigated conditions (irrigation

based on ETO and phenology)

Opportunities to increase WP and save irrigation water

through optimization of the irrigation management

Each point is the average of 3 years (2005-2007); circles indicate the approximate distribution of each

category. Vertical and horizontal bars indicate ± SD of the mean.

Reported yield and actual water

supply under pivot ( ) and

gravity ( Δ ) irrigation systems.

128

160

192

224

256

288

16 24 32 40 48 56


Gra

in y

ield

(b

u a

c-1

)

11 bu ac-in-1

8 bu ac-in-1

Actual

Pivot

Actual

Surface

37,819 ac-ft yr-1

Optimal

irrigation

20,639 ac-ft yr-1

Limited

irrigation

33,252 ac-ft yr-1

Total saving: 91,710 ac-ft y-1

(~32% of current water use in corn!)


120

140

160

180

200

N f

ert

iliz

er

rate

(lb

N a

c-1

)

0.5

0.8

1.0

1.3

1.5

1.8

NU

E (

bu

lb-1

N f

ert

iliz

er)

CS CV CS CV

CS CV CS CV

120

140

160

180

200

N f

ert

iliz

er

rate

(lb

N a

c-1

)

0.5

0.8

1.0

1.3

1.5

1.8

NU

E (

bu

lb-1

N f

ert

iliz

er)

CS CV CS CV

CS CV CS CV

Conventional (CT): disk

Conservation (CS): strip-, ridge-, and no-till

* Based on management data collected from 123 fields in the Tri-Basin NRD during 2005-2007 seasons.

Values above bars indicate average corn grain yield (bu ac-1) for each rotation x tillage combination

Tillage system:

• Although N rate is above U.S. average,

yields and NUE are higher especially under

soybean-corn rotation due to higher yields

and lower N rate than continuous corn.

• No difference in N fertilizer rate under

continuous corn with CS or CV tillage; under

soybean-corn rotation, N fertilizer tended to

be higher under CS than CV.

• NUE tended to be higher under conventional

tillage due to (i) higher yields at the same N

rate under continuous corn and (ii) same

yield with lower N rate under soybean-corn

rotation.

U.S. averages

Corn yield, rate of N fertilizer, and nitrogen use efficiency (NUE)*


203 212

215 214

Modified from Grassini et al. (2010): Field Crops Res. 48

Net energy yield (GJ ha-1

)

120 140 160 180 200G

WP

i (k

g C

O2e

Mg

-1 g

rain

)

100

200

300

400

GWP (kg CO2e ha

-1)

500 1875 3250 4625

Energy input rate (GJ ha-1

)

5 15 25 35 45 55

Gra

in y

ield

(M

g h

a-1

)

4

8

12

16

NE

R =

8.4

NER =

4.5

NE

R =

6.5

GW

Pi = 1

74

GW

Pi = 2

18

GW

Pi =

317

y = 808 - 3.6xr = -0.75; P<0.001

(a) (b) (c)

2005

2006

2007

Tri-Basin NRD(n = 123):

Nebraska (13, 22)

Iowa (13, 22)

Illinois (13, 22)

USA (5, 18, 19, 30)


Estimates from previous studies mostly on rainfed corn production

Patricio Grassini and Kenneth G. Cassman, Univ. of Nebraska

Proceedings of the National Academy of Science (Jan. 2012)

Take home from Environmental Assessment of Irrigated Corn in Nebraska

• Although NE irrigated corn high levels of N fertilizer, water, and energy input, compared to rainfed corn it has: – Greater N fertilizer efficiency

– Greater net energy yield

– Smaller global warming potential intensity

• Good news for modern, science-based agriculture – Goals of high yield, high input efficiency, large energy

yield, and minimal GHG emissions are complementary

– Significant potential to further improve environmental performance of high-yield systems


Conclusions • Conventional agriculture is continually behind the

curve and on the defensive in relation to environmental concerns and standards

– Agenda and metrics are established by those who know little about agriculture or care about its fate

– “Crisis mode “ in response to bad science, but negative perceptions never seem to be overturned

– Increased monitoring of environmental performance is going to happen, driven by the food industry and public perceptions about impact of agriculture on the environment

– Ironically, high-yield, science-based agriculture is actually quite good and getting better in terms of fertilizer, water, energy efficiency, and CC mitigation

• There is a tremendous opportunity to set the environmental agenda if fertilizer, seed, and ag equipment companies provide leadership


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